Hermes Agent Tutorial

Hermes Agent was developed by Nous Research and officially released in February 2026Open-source self-evolving AI Agent, released under the MIT License.

Hermes runs on your own server or local machine, maintains persistent memory across sessions, and actively learns and extracts reusable skills after completing each task—Smarter with use。

Nous Research Official Slogan --The agent that grows with you.

Who is this tutorial for?

This tutorial is intended for the following types of readers:

  • Developers / EngineersWant a local AI assistant that persistently remembers project context and automatically accumulates workflow experience — instead of having to re-explain the codebase structure, naming conventions, and deployment process from scratch every session.

  • ResearchersNeed an intelligent assistant that can track research progress across sessions, automatically organize literature information, and carry out long-term research tasks.

  • Efficiency tool enthusiastsHope to truly embed AI Agents into daily workflows — integrating with commonly used platforms like Telegram, Slack, and Discord, and setting up scheduled automated tasks.

  • AI / ML practitionersResearchers interested in Agent architecture, or who need to use Hermes to batch-generate tool-calling trajectories for reinforcement learning training data.

  • Users who value data privacyAll data remains on the local machine — no telemetry, no tracking, no cloud lock-in.

Prerequisites for reading

This tutorialNot requiredAI research background or deep machine learning knowledge. You need:

Skills Requirement level Description
Basic command line operations Required Be able to execute commands in the terminal and set environment variables
Python basics Familiarity is enough Know how to install packages with pip and read simple scripts
Basic API concepts Familiarity is enough Know what an API Key is and how to obtain one
Git basics Optional Used in advanced chapters (plugin development)

Operating system requirements: Linux, macOS, or Windows WSL2 (choose one of three).

Core features

Hermes features:

  • 🧠 Persistent memory— Cross-session three-layer memory + Honcho user modeling, understands you better the more you use it
  • ⚡ Skill system— Automatically create/improve Skills/learnLearn commands from documentation with one click
  • 🔌 Rich tools— 70+ built-in tools: file system, web browsing, code execution, vision, voice
  • 🌐 Multi-platform access— Telegram, Discord, Slack, WhatsApp, Signal, and 15+ platforms
  • 🔒 Privacy first— All data stored locally, no telemetry, no forced cloud dependency
  • 🤖 Model-agnostic— Supports 200+ models, switch with one command
  • ⏰ Scheduled tasks— Built-in Cron scheduling, supports cross-platform message delivery
  • 🔬 Research-ready— Batch trajectory generation, ShareGPT format export, RL training integration

Related resources

Official resources

Resources Links
Official documentation hermes-agent.nousresearch.com/docs
GitHub repository github.com/NousResearch/hermes-agent
Skill Community Hub agentskills.io
Model providers Nous Portal

Learning resources

Existing platforms and popular frameworks:

Core requirements Recommended tools Key advantages
Miaoda, generate applications from one sentence Miaoda Official Website Zero code — describe your requirement in one sentence and the app is generated
Dazi, desktop-level AI agent Dazi Official Website Desktop-level AI agent for individuals and teams; can see the screen, operate software, and process files
MonkeyCode, an AI application development platform MonkeyCode Official Website Create tasks directly in the platform, let the AI code, and use the terminal, file management, and preview in the cloud development environment
Xiaoyunque (Little Lark), CapCut's AI video generation Jianying - Little Skylark ByteDance's self-developed Seedance 2.0 video model + Seedream 5.0 image model, paired with the Doubao large model for copy understanding
QoderWork, a desktop-class AI Agent QoderWork You state the requirements, it delivers the results.
Automated triggering and system integration n8n Broad integration, self-hostable, connects to common internal systems
Developer-controllable deep customization Dify
LangChain 		The former provides a complete open-source solution; the latter is suited for building complex reasoning chains
Multi-role collaboration and task decomposition AutoGen
CrewAI 		The former emphasizes dynamic collaboration; the latter drives workflows through a clear role system
Autonomous task execution Agent AutoGPT An early phenomenal open-source Agent project, emphasizing goal-driven autonomous task decomposition and looped execution (Plan → Execute → Reflect)
other extensions